package org.apache.lucene.sandbox.queries;

/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

import java.io.IOException;
import java.io.StringReader;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.HashSet;
import java.util.Iterator;

import org.apache.lucene.analysis.Analyzer;
import org.apache.lucene.analysis.TokenStream;
import org.apache.lucene.analysis.tokenattributes.CharTermAttribute;
import org.apache.lucene.index.IndexReader;
import org.apache.lucene.index.MultiFields;
import org.apache.lucene.index.Term;
import org.apache.lucene.index.Terms;
import org.apache.lucene.search.*;
import org.apache.lucene.search.similarities.TFIDFSimilarity;
import org.apache.lucene.search.similarities.DefaultSimilarity;
import org.apache.lucene.util.AttributeSource;
import org.apache.lucene.util.BytesRef;
import org.apache.lucene.util.PriorityQueue;

/**
 * Fuzzifies ALL terms provided as strings and then picks the best n differentiating terms.
 * In effect this mixes the behaviour of FuzzyQuery and MoreLikeThis but with special consideration
 * of fuzzy scoring factors.
 * This generally produces good results for queries where users may provide details in a number of 
 * fields and have no knowledge of boolean query syntax and also want a degree of fuzzy matching and
 * a fast query.
 * 
 * For each source term the fuzzy variants are held in a BooleanQuery with no coord factor (because
 * we are not looking for matches on multiple variants in any one doc). Additionally, a specialized
 * TermQuery is used for variants and does not use that variant term's IDF because this would favour rarer 
 * terms eg misspellings. Instead, all variants use the same IDF ranking (the one for the source query 
 * term) and this is factored into the variant's boost. If the source query term does not exist in the
 * index the average IDF of the variants is used.
 */
public class FuzzyLikeThisQuery extends Query
{
    // TODO: generalize this query (at least it should not reuse this static sim!
    // a better way might be to convert this into multitermquery rewrite methods.
    // the rewrite method can 'average' the TermContext's term statistics (docfreq,totalTermFreq) 
    // provided to TermQuery, so that the general idea is agnostic to any scoring system...
    static TFIDFSimilarity sim=new DefaultSimilarity();
    Query rewrittenQuery=null;
    ArrayList<FieldVals> fieldVals=new ArrayList<FieldVals>();
    Analyzer analyzer;
    
    ScoreTermQueue q;
    int MAX_VARIANTS_PER_TERM=50;
    boolean ignoreTF=false;
    private int maxNumTerms;

    @Override
    public int hashCode() {
      final int prime = 31;
      int result = super.hashCode();
      result = prime * result + ((analyzer == null) ? 0 : analyzer.hashCode());
      result = prime * result
          + ((fieldVals == null) ? 0 : fieldVals.hashCode());
      result = prime * result + (ignoreTF ? 1231 : 1237);
      result = prime * result + maxNumTerms;
      return result;
    }

    @Override
    public boolean equals(Object obj) {
      if (this == obj)
        return true;
      if (obj == null)
        return false;
      if (getClass() != obj.getClass())
        return false;
      if (!super.equals(obj)) {
        return false;
      }
      FuzzyLikeThisQuery other = (FuzzyLikeThisQuery) obj;
      if (analyzer == null) {
        if (other.analyzer != null)
          return false;
      } else if (!analyzer.equals(other.analyzer))
        return false;
      if (fieldVals == null) {
        if (other.fieldVals != null)
          return false;
      } else if (!fieldVals.equals(other.fieldVals))
        return false;
      if (ignoreTF != other.ignoreTF)
        return false;
      if (maxNumTerms != other.maxNumTerms)
        return false;
      return true;
    }


    /**
     * 
     * @param maxNumTerms The total number of terms clauses that will appear once rewritten as a BooleanQuery
     */
    public FuzzyLikeThisQuery(int maxNumTerms, Analyzer analyzer)
    {
        q=new ScoreTermQueue(maxNumTerms);
        this.analyzer=analyzer;
        this.maxNumTerms = maxNumTerms;
    }

    class FieldVals
    {
      String queryString;
      String fieldName;
      float minSimilarity;
      int prefixLength;
    public FieldVals(String name, float similarity, int length, String queryString)
    {
      fieldName = name;
      minSimilarity = similarity;
      prefixLength = length;
      this.queryString = queryString;
    }

    @Override
    public int hashCode() {
      final int prime = 31;
      int result = 1;
      result = prime * result
          + ((fieldName == null) ? 0 : fieldName.hashCode());
      result = prime * result + Float.floatToIntBits(minSimilarity);
      result = prime * result + prefixLength;
      result = prime * result
          + ((queryString == null) ? 0 : queryString.hashCode());
      return result;
    }

    @Override
    public boolean equals(Object obj) {
      if (this == obj)
        return true;
      if (obj == null)
        return false;
      if (getClass() != obj.getClass())
        return false;
      FieldVals other = (FieldVals) obj;
      if (fieldName == null) {
        if (other.fieldName != null)
          return false;
      } else if (!fieldName.equals(other.fieldName))
        return false;
      if (Float.floatToIntBits(minSimilarity) != Float
          .floatToIntBits(other.minSimilarity))
        return false;
      if (prefixLength != other.prefixLength)
        return false;
      if (queryString == null) {
        if (other.queryString != null)
          return false;
      } else if (!queryString.equals(other.queryString))
        return false;
      return true;
    }
    


    }
    
    /**
     * Adds user input for "fuzzification" 
     * @param queryString The string which will be parsed by the analyzer and for which fuzzy variants will be parsed
     * @param minSimilarity The minimum similarity of the term variants (see FuzzyTermsEnum)
     * @param prefixLength Length of required common prefix on variant terms (see FuzzyTermsEnum)
     */
    public void addTerms(String queryString, String fieldName,float minSimilarity, int prefixLength) 
    {
      fieldVals.add(new FieldVals(fieldName,minSimilarity,prefixLength,queryString));
    }


  private void addTerms(IndexReader reader, FieldVals f) throws IOException {
    if (f.queryString == null) return;
    TokenStream ts = analyzer.tokenStream(f.fieldName, new StringReader(f.queryString));
    CharTermAttribute termAtt = ts.addAttribute(CharTermAttribute.class);

    int corpusNumDocs = reader.numDocs();
    HashSet<String> processedTerms = new HashSet<String>();
    ts.reset();
    final Terms terms = MultiFields.getTerms(reader, f.fieldName);
    if (terms == null) {
      return;
    }
    while (ts.incrementToken()) {
      String term = termAtt.toString();
      if (!processedTerms.contains(term)) {
        processedTerms.add(term);
        ScoreTermQueue variantsQ = new ScoreTermQueue(MAX_VARIANTS_PER_TERM); //maxNum variants considered for any one term
        float minScore = 0;
        Term startTerm = new Term(f.fieldName, term);
        AttributeSource atts = new AttributeSource();
        MaxNonCompetitiveBoostAttribute maxBoostAtt =
            atts.addAttribute(MaxNonCompetitiveBoostAttribute.class);
        SlowFuzzyTermsEnum fe = new SlowFuzzyTermsEnum(terms, atts, startTerm, f.minSimilarity, f.prefixLength);
        //store the df so all variants use same idf
        int df = reader.docFreq(startTerm);
        int numVariants = 0;
        int totalVariantDocFreqs = 0;
        BytesRef possibleMatch;
        BoostAttribute boostAtt =
            fe.attributes().addAttribute(BoostAttribute.class);
        while ((possibleMatch = fe.next()) != null) {
          numVariants++;
          totalVariantDocFreqs += fe.docFreq();
          float score = boostAtt.getBoost();
          if (variantsQ.size() < MAX_VARIANTS_PER_TERM || score > minScore) {
            ScoreTerm st = new ScoreTerm(new Term(startTerm.field(), BytesRef.deepCopyOf(possibleMatch)), score, startTerm);
            variantsQ.insertWithOverflow(st);
            minScore = variantsQ.top().score; // maintain minScore
          }
          maxBoostAtt.setMaxNonCompetitiveBoost(variantsQ.size() >= MAX_VARIANTS_PER_TERM ? minScore : Float.NEGATIVE_INFINITY);
        }

        if (numVariants > 0) {
          int avgDf = totalVariantDocFreqs / numVariants;
          if (df == 0)//no direct match we can use as df for all variants
          {
            df = avgDf; //use avg df of all variants
          }

          // take the top variants (scored by edit distance) and reset the score
          // to include an IDF factor then add to the global queue for ranking
          // overall top query terms
          int size = variantsQ.size();
          for (int i = 0; i < size; i++) {
            ScoreTerm st = variantsQ.pop();
            st.score = (st.score * st.score) * sim.idf(df, corpusNumDocs);
            q.insertWithOverflow(st);
          }
        }
      }
    }
    ts.end();
    ts.close();
  }

  @Override
    public Query rewrite(IndexReader reader) throws IOException
    {
        if(rewrittenQuery!=null)
        {
            return rewrittenQuery;
        }
        //load up the list of possible terms
        for (Iterator<FieldVals> iter = fieldVals.iterator(); iter.hasNext(); ) {
          FieldVals f = iter.next();
          addTerms(reader, f);
        }
      //clear the list of fields
        fieldVals.clear();
        
        BooleanQuery bq=new BooleanQuery();
        
        
        //create BooleanQueries to hold the variants for each token/field pair and ensure it
        // has no coord factor
        //Step 1: sort the termqueries by term/field
        HashMap<Term,ArrayList<ScoreTerm>> variantQueries=new HashMap<Term,ArrayList<ScoreTerm>>();
        int size = q.size();
        for(int i = 0; i < size; i++)
        {
          ScoreTerm st = q.pop();
          ArrayList<ScoreTerm> l= variantQueries.get(st.fuzziedSourceTerm);
          if(l==null)
          {
              l=new ArrayList<ScoreTerm>();
              variantQueries.put(st.fuzziedSourceTerm,l);
          }
          l.add(st);
        }
        //Step 2: Organize the sorted termqueries into zero-coord scoring boolean queries
        for (Iterator<ArrayList<ScoreTerm>> iter = variantQueries.values().iterator(); iter.hasNext();)
        {
            ArrayList<ScoreTerm> variants = iter.next();
            if(variants.size()==1)
            {
                //optimize where only one selected variant
                ScoreTerm st= variants.get(0);
                Query tq = ignoreTF ? new ConstantScoreQuery(new TermQuery(st.term)) : new TermQuery(st.term, 1);
                tq.setBoost(st.score); // set the boost to a mix of IDF and score
                bq.add(tq, BooleanClause.Occur.SHOULD); 
            }
            else
            {
                BooleanQuery termVariants=new BooleanQuery(true); //disable coord and IDF for these term variants
                for (Iterator<ScoreTerm> iterator2 = variants.iterator(); iterator2
                        .hasNext();)
                {
                    ScoreTerm st = iterator2.next();
                    // found a match
                    Query tq = ignoreTF ? new ConstantScoreQuery(new TermQuery(st.term)) : new TermQuery(st.term, 1);                    
                    tq.setBoost(st.score); // set the boost using the ScoreTerm's score
                    termVariants.add(tq, BooleanClause.Occur.SHOULD);          // add to query                    
                }
                bq.add(termVariants, BooleanClause.Occur.SHOULD);          // add to query
            }
        }
        //TODO possible alternative step 3 - organize above booleans into a new layer of field-based
        // booleans with a minimum-should-match of NumFields-1?
        bq.setBoost(getBoost());
        this.rewrittenQuery=bq;
        return bq;
    }
    
    //Holds info for a fuzzy term variant - initially score is set to edit distance (for ranking best
    // term variants) then is reset with IDF for use in ranking against all other
    // terms/fields
    private static class ScoreTerm{
        public Term term;
        public float score;
        Term fuzziedSourceTerm;
        
        public ScoreTerm(Term term, float score, Term fuzziedSourceTerm){
          this.term = term;
          this.score = score;
          this.fuzziedSourceTerm=fuzziedSourceTerm;
        }
      }
      
      private static class ScoreTermQueue extends PriorityQueue<ScoreTerm> {        
        public ScoreTermQueue(int size){
          super(size);
        }
        
        /* (non-Javadoc)
         * @see org.apache.lucene.util.PriorityQueue#lessThan(java.lang.Object, java.lang.Object)
         */
        @Override
        protected boolean lessThan(ScoreTerm termA, ScoreTerm termB) {
          if (termA.score== termB.score)
            return termA.term.compareTo(termB.term) > 0;
          else
            return termA.score < termB.score;
        }
        
      }    
      
    /* (non-Javadoc)
     * @see org.apache.lucene.search.Query#toString(java.lang.String)
     */
    @Override
    public String toString(String field)
    {
        return null;
    }


  public boolean isIgnoreTF()
  {
    return ignoreTF;
  }


  public void setIgnoreTF(boolean ignoreTF)
  {
    this.ignoreTF = ignoreTF;
  }
    
}
